Galgotias College, also known as the Galgotias Campus 1 is the corporate headquarters of Galgotias Educational Institutions (GEI) in Knowledge Park II, Greater Noida, Uttar Pradesh, India, comprising three institutes. Established in 1999 as Galgotias Institute of Management and Technology (GIMT), it later included Galgotias College of Engineering and Technology (GCET) and Galgotias College of Pharmacy (GCP).
The CoxCd1−xFe2O4 (where x = 0.0, 0.25, 0.5, 0.75, and 1.0) nanoferrites were formed successfully using the co-precipitation technique. The optical, structural, and magnetic characteristics of the formed nanoferrites were studied. The powder XRD results revealed the formation of single-phase cubic spinel ferrites with crystallite sizes ranging from 35 to 77 nm. The FTIR analysis showed two significant fundamental bands corresponding to the intrinsic stretching vibrations of metal atoms located at tetrahedral and octahedral sites. The Raman scattering results confirmed the IR results and revealed the enhanced localized disorder at both tetrahedral and octahedral sublattices with an increase in Cobalt (Co) content. The optical band gap was determined by the UV-visible spectroscopy. The optical band gap value estimated from Tauc plots was found to decrease from 3.08 eV to 2.78 eV with increasing Co content. The effects of Co2+ substitution on Mossbauer parameters such as line width, isomer shift, quadrupole splitting, and hyperfine magnetic field were also investigated. The magnetic hysteresis (M-H) curve was measured for each manufactured sample at room temperature using a SQUID-based magnetometer. Saturation magnetization (Ms) was observed to increase from 0.29 to 44.96 (emu/g) with an increase in Co concentration. Co-Cd ferrites displayed (S-shaped) hysteresis loops, indicating ferromagnetic behavior of the compounds for x = 0.5 to 1.0. The coercivity of pure cobalt ferrite CoFe2O4 was larger than that of the Co-doped cadmium ferrite. The Mossbauer analysis revealed the superparamagnetic behavior of the present ferrite composition for x = 0.25. The paramagnetic doublets in Mossbauer spectrum with quadruple splitting (Δ) = 0.871 mm/s and line width (Γ) = 0.644 mm/s are assigned as core.
Radio frequency (RF) energy harvesting (EH) for wireless networks offers a green and sustainable solution, enhancing energy efficiency and ensuring the longevity of devices, even in remote, inaccessible locations. Such RF-EH mechanisms are utilized in simultaneous wireless information and power transfer systems. This study evaluates wireless information and power transfer in two-way relay networks, where two source nodes communicate directly and via a battery-powered relay node. This research presents new analytical results assuming the time switching protocol with amplify-and-forward relaying, and selection combining receiver to combine direct-link and relay-assisted signals over generalized η -μ fading channels. Our study derives novel analytical formulations for key performance measures, namely outage probability, system throughput, system energy efficiency, and the average symbol error rate of a generalized rectangular quadrature amplitude modulation scheme. In addition, an asymptotic analysis of the outage probability is carried out to determine the diversity order of the system. The resulting expressions are formulated as rapidly converging infinite series, which can be efficiently evaluated using only a limited number of terms. The influence of key system parameters, such as fading characteristics, target rate threshold, time switching ratio, energy conversion efficiency, relay location, and constellation order, is thoroughly examined. The accuracy of the proposed analytical framework is verified through numerical evaluation and Monte Carlo simulations, demonstrating excellent agreement between analytical and simulation results.
Targeted drug delivery within advanced systems represents one of the most promising strategies for achieving superior therapeutic outcomes. Among the emerging materials investigated for this purpose, nanocomposites have demonstrated exceptional potential in the design of highly selective and efficient delivery platforms. In particular, titanium dioxide (TiO₂) nanoparticles (NPs) have attracted considerable scientific interest due to their unique physicochemical characteristics, stability, and biocompatibility, which make them highly suitable for diverse biomedical applications. However, comprehensive biological characterization remains essential to fully harness their capabilities for future translational research. In the present study, titanium NPs were synthesized using the angle deposition technique, a controlled fabrication method that enables precise NPs formation. The NPs were deposited onto a glass substrate and subsequently extracted through ultrasonication to obtain a stable NP suspension for experimental analysis. The results obtained from the characterization and biological assessment of the synthesized NPs were highly encouraging. In vitro investigations conducted using erythrocytes and platelet-rich plasma demonstrated promising interactions relevant to targeted drug delivery mechanisms. Notably, the TiO₂ exhibited favorable compatibility with blood components and contributed to measurable enhancement in platelet morphology and size through natural sensing interactions. To ensure high analytical precision, advanced image processing techniques were employed for accurate measurement and morphological evaluation of platelets and red blood cells (RBCs). The findings of this study strongly suggest that TiO₂ NPs possess significant potential as targeted drug delivery agents. Their ability to interact effectively with biological systems indicates promising applications in the treatment of critical diseases such as cancer, inflammatory disorders, osteoporosis, and thrombosis. These results provide a compelling foundation for further investigation into TiO₂-based nanomaterials as next-generation therapeutic delivery platforms.
Female mortality is frequently caused by ovarian cancer (OC). Because of its late detection, ovarian cancer seems to have a low survival rate, and new techniques are required for its early identification. One of the more prevalent gynecologic cancers is ovarian cancer. The various diagnoses of ovarian cancer depend on the efficient classification of the various forms. Patients with ovarian tumours require accurate diagnosis. When compared to a deep convolutional neural network, previous neural networks are an outmoded technology that offers fewer characteristics, which demonstrates that deep convolutional layers supply essential and healthy features. To get over these limitations, ovarian tumours are identified using the krill herd optimization-based convolutional neural network (KHO-CNN) mechanism, a novel optimized deep neural network approach. The system analyses datasets related to ultrasound-detected ovarian cancer. The obtained real-world ultrasound images of ovarian cancer also contain additional noise, which is removed using a Wavelet Transform. An enhanced KHO model has been used in the segmentation process. Features were extracted by use of a local binary pattern. Ovarian tumours are classified as benign, malignant, or normal by the KHO-CNN. To identify ovarian cancers using deep learning techniques that utilize optimised convolutional neural networks, this model was developed and utilised with a set.
In intelligent transportation system, traffic surveillance is an important topic. One challenging problem for complex urban traffic surveillance system is robust vehicle detection and tracking. Therefore, in this paper, we develop a two-stage approach for moving vehicle detection system. The proposed system mainly consists of two stages such as hypothesis generation (HG) and hypothesis verification (HV). In the first step, we generate the hypotheses using shadows under vehicles is darker than road region concept. In the second step, we verify hypotheses generated in the first step whether correct or not using optimal feedforward neural network (OFFNN). Here, to extract vehicle features, we utilize two types of histogram orientation gradients descriptors (HOG). In training stage, the histogram orientation gradients features are given to the OFFNN classifier. The weights corresponding FFNN is optimally select using improved grasshopper optimization algorithm (IGOA). The experimental results show that the proposed moving vehicle detection system performs better accuracy compare to other methods.
